| Type: | Package |
| Title: | Generate High-Dimensional Binary Data with Correlation Structures |
| Version: | 1.0.0 |
| Author: | Wei Jiang [aut], Shuang Song [aut, cre], Lin Hou [aut] and Hongyu Zhao [aut] |
| Maintainer: | Shuang Song <song-s19@mails.tsinghua.edu.cn> |
| Description: | We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-11-14 08:57:58 UTC; lenovo |
| Repository: | CRAN |
| Date/Publication: | 2020-11-14 09:20:02 UTC |
Main function
Description
The main function of our package, through which we can simulate correlated binary data under different settings.
Usage
cBern(n, p, rho, type, k = NULL)
Arguments
n |
number of observations |
p |
the vector of marginal probabilities with dimension m |
rho |
For the first three types, rho is either a non-negative value indecating the shared correlation coefficient or and m-1 vector indicating the correlation coefficients between adjacent variables. For the general case, rho should be a list, the i-th element of which specifies the coefficients on the i-th minor diagnal. |
type |
including 4 types. type="exchange" type="DCP" type="1-dependent" type="General" |
k |
(for 'General' use only). The number of layers setting for k-dependent structure. k=m-1 for the general case. |
Value
an n*p matrix of binary data
References
Jiang, W., Song, S., Hou, L. and Zhao, H. A set of efficient methods to generate high-dimensional binary data with specified correlation structures. The American Statistician. DOI:10.1080/00031305.2020.1816213
See Also
Examples
X <- cBern(10, rep(0.5,3), 0.5, type="exchange")
X <- cBern(10, rep(0.5,3), c(0.2,0.2), type="DCP")
X <- cBern(5, c(0.4,0.5,0.6), c(0.2,0.3), type="1-dependent")
rho <- list()
rho[[1]] <- c(0.2,0.3)
rho[[2]] <- 0.1
X <- cBern(2, c(0.7,0.8,0.9),rho=rho,type="General", k=2)
Generate binary data with 1-dependent correlated structure
Description
Equivalent to cBern(n, p, rho, type="1-dependent")
Usage
cBern1dep(n, p, rho)
Arguments
n |
number of observations |
p |
the vector of marginal probabilities with dimension m |
rho |
either a non-negative value indecating the shared correlation coefficient or and m-1 vector indicating the correlation coefficients between adjacent variables. |
Value
an n*p matrix of binary data
Examples
X <- cBern1dep(5, c(0.4,0.5,0.6), c(0.2,0.3))
Generate binary data with decaying-product correlated structure
Description
Equivalent to cBern(n, p, rho, type="DCP")
Usage
cBernDCP(n, p, rho)
Arguments
n |
number of observations |
p |
the vector of marginal probabilities with dimension m |
rho |
either a non-negative value indecating the shared correlation coefficient or and m-1 vector indicating the correlation coefficients between adjacent variables. |
Value
an n*p matrix of binary data
Examples
X <- cBernDCP(10, rep(0.5,3), c(0.2,0.2))
Generate binary data with exchangeable correlated structure
Description
Equivalent to cBern(n, p, rho, type="exchange")
Usage
cBernEx(n, p, rho)
Arguments
n |
number of observations |
p |
the vector of marginal probabilities with dimension m |
rho |
a non-negative value indecating the shared correlation coefficient |
Value
an n*p matrix of binary data
Examples
X <- cBernEx(10, rep(0.5,3), 0.5)
To calculate the maximal allowed correlations max for using cBern1dep to generate binary data with 1-dependent structure
Description
To calculate the maximal allowed correlations max for using cBern1dep to generate binary data with 1-dependent structure
Usage
rhoMax1dep(p)
Arguments
p |
the vector of marginal probabilities with dimension m |
Value
an (m-1)-dimensional vector rho, which is the maximum the correlation between the adjacent variables
For calculating the maximal allowed correlations max for binary data with decaying-product structure.
Description
For calculating the maximal allowed correlations max for binary data with decaying-product structure.
Usage
rhoMaxDCP(p)
Arguments
p |
marginal probabilities |
Value
an (m-1)-dimensional vector rho, which is the maximum the correlation between the adjacent variables
For calculating the maximal allowed correlation coefficient for binary data with exchangeable structure.
Description
For calculating the maximal allowed correlation coefficient for binary data with exchangeable structure.
Usage
rhoMaxEx(p)
Arguments
p |
the vector of marginal probabilities with dimension m |
Value
the maximal allowed correlation coefficient